Adaptive alarm system

ABSTRACT

An adaptive alarm system is responsive to a physiological parameter so as to generate an alarm threshold that adapts to baseline drift in the parameter and reduce false alarms without a corresponding increase in missed true alarms. The adaptive alarm system has a parameter derived from a physiological measurement system using a sensor in communication with a living being. A baseline processor calculates a parameter baseline from a parameter trend. Parameter limits specify an allowable range of the parameter. An adaptive threshold processor calculates an adaptive threshold from the parameter baseline and the parameter limits. An alarm generator is responsive to the parameter and the adaptive threshold so as to trigger an alarm indicative of the parameter crossing the adaptive threshold. The adaptive threshold is responsive to the parameter baseline so as to increase in value as the parameter baseline drifts to a higher parameter value and to decrease in value as the parameter baseline drifts to a lower parameter value.

PRIORITY CLAIM TO RELATED PROVISIONAL APPLICATIONS

The present application claims priority benefit under 35 U.S.C. §119(e)to U.S. Provisional Patent Application Ser. No. 61/309,419, filed Mar.1, 2010 titled Adaptive Threshold Alarm System; and U.S. ProvisionalPatent Application Ser. No. 61/328,630, filed Apr. 27, 2010 titledAdaptive Alarm System; all of the above-cited provisional patentapplications are hereby incorporated by reference herein.

BACKGROUND OF THE INVENTION

Pulse oximetry systems for measuring constituents of circulating bloodhave gained rapid acceptance in a wide variety of medical applications,including surgical wards, intensive care and neonatal units, generalwards, home care, physical training, and virtually all types ofmonitoring scenarios. A pulse oximetry system generally includes anoptical sensor applied to a patient, a monitor for processing sensorsignals and displaying results and a patient cable electricallyinterconnecting the sensor and the monitor. A pulse oximetry sensor haslight emitting diodes (LEDs), typically one emitting a red wavelengthand one emitting an infrared (IR) wavelength, and a photodiode detector.The emitters and detector are typically attached to a finger, and thepatient cable transmits drive signals to these emitters from themonitor. The emitters respond to the drive signals to transmit lightinto the fleshy fingertip tissue. The detector generates a signalresponsive to the emitted light after attenuation by pulsatile bloodflow within the fingertip. The patient cable transmits the detectorsignal to the monitor, which processes the signal to provide a numericalreadout of physiological parameters such as oxygen saturation (SpO₂) andpulse rate.

SUMMARY OF THE INVENTION

Conventional pulse oximetry assumes that arterial blood is the onlypulsatile blood flow in the measurement site. During patient motion,venous blood also moves, which causes errors in conventional pulseoximetry. Advanced pulse oximetry processes the venous blood signal soas to report true arterial oxygen saturation and pulse rate underconditions of patient movement. Advanced pulse oximetry also functionsunder conditions of low perfusion (small signal amplitude), intenseambient light (artificial or sunlight) and electrosurgical instrumentinterference, which are scenarios where conventional pulse oximetrytends to fail.

Advanced pulse oximetry is described in at least U.S. Pat. Nos.6,770,028; 6,658,276; 6,157,850; 6,002,952; 5,769,785 and 5,758,644,which are assigned to Masimo Corporation (“Masimo”) of Irvine, Calif.and are incorporated by reference herein. Corresponding low noiseoptical sensors are disclosed in at least U.S. Pat. Nos. 6,985,764;6,813,511; 6,792,300; 6,256,523; 6,088,607; 5,782,757 and 5,638,818,which are also assigned to Masimo and are also incorporated by referenceherein. Advanced pulse oximetry systems including Masimo SET® low noiseoptical sensors and read through motion pulse oximetry monitors formeasuring SpO₂, pulse rate (PR) and perfusion index (PI) are availablefrom Masimo. Optical sensors include any of Masimo LNOP®, LNCS®,SofTouch™ and Blue™ adhesive or reusable sensors. Pulse oximetrymonitors include any of Masimo Rad-8®, Rad-5®, Rad®-5v or SatShare®monitors.

Advanced blood parameter measurement systems are described in at leastU.S. Pat. No. 7,647,083, filed Mar. 1, 2006, titled Multiple WavelengthSensor Equalization; U.S. Pat. No. 7,729,733, filed Mar. 1, 2006, titledConfigurable Physiological Measurement System; U.S. Pat. Pub. No.2006/0211925, filed Mar. 1, 2006, titled Physiological ParameterConfidence Measure and U.S. Pat. Pub. No. 2006/0238358, filed Mar. 1,2006, titled Noninvasive Multi-Parameter Patient Monitor, all assignedto Masimo Laboratories, Irvine, Calif. (Masimo Labs) and allincorporated by reference herein. An advanced parameter measurementsystem that includes acoustic monitoring is described in U.S. Pat. Pub.No. 2010/0274099, filed Dec. 21, 2009, titled Acoustic Sensor Assembly,assigned to Masimo and incorporated by reference herein.

Advanced blood parameter measurement systems include Masimo Rainbow®SET, which provides measurements in addition to SpO₂, such as totalhemoglobin (SpHb™), oxygen content (SpOC™), methemoglobin (SpMet®),carboxyhemoglobin (SpCO®) and PVI®. Advanced blood parameter sensorsinclude Masimo Rainbow® adhesive, ReSposable™ and reusable sensors.Advanced blood parameter monitors include Masimo Radical-7™, Rad-87™ andRad-57™ monitors, all available from Masimo. Advanced parametermeasurement systems may also include acoustic monitoring such asacoustic respiration rate (RRa™) using a Rainbow Acoustic Sensor™ andRad-87™ monitor, available from Masimo. Such advanced pulse oximeters,low noise sensors and advanced physiological parameter measurementsystems have also gained rapid acceptance in a wide variety of medicalapplications, including surgical wards, intensive care and neonatalunits, general wards, home care, physical training, and virtually alltypes of monitoring scenarios.

FIGS. 1-3 illustrate problems and issues associated with physiologicalparameter measurement systems having fixed threshold alarm schemas. FIG.1 illustrates a lower-limit, fixed-threshold alarm schema with respectto an oxygen saturation (SpO₂) parameter. Two alarm thresholds, D_(L)(delay) and ND_(L) (no delay), are defined. If oxygen saturation fallsbelow D_(L) for a time delay greater than TD, an alarm is triggered. Ifoxygen saturation falls below ND_(L) an alarm is immediately triggered.D_(L) 120 is typically set around or somewhat above 90% oxygensaturation and ND_(L) 130 is typically set at 5% to 10% below D_(L). Forexample, say a person's oxygen saturation 110 drops below D_(L) 120 att=t₁ 162 and stays below D_(L) for at least a time delay TD 163. Thistriggers a delayed alarm 140 at t=t₂ 164, where t₂=t₁+TD. The alarm 140remains active until oxygen saturation 110 rises above D_(L) 120 at t=t₃166. As another example, say that oxygen saturation 110 then drops belowND_(L) 130, which triggers an immediate alarm 150 at t=t4 168. The alarm150 remains active until oxygen saturation 110 rises above D_(L) 120 att=t₅ 169.

FIG. 2 illustrates an upper-limit, fixed-threshold alarm schema withrespect to an oxygen saturation (SpO₂) parameter. This alarm scenario isparticularly applicable to the avoidance of ROP (retinopathy ofprematurity). Again, two alarm thresholds, D_(U) (delay) and ND_(U) (nodelay), are defined. D_(U) 220 might be set at or around 85% oxygensaturation and ND_(U) 230 might be set at or around 90% oxygensaturation. For example, a neonate's oxygen saturation 210 rises aboveD_(U) 220 at t=t₁ 262 and stays above D_(U) for at least a time delay TD263. This triggers a delayed alarm 240 at t=t₂ 264, where t₂=t₁+TD. Thealarm 240 remains active until oxygen saturation 210 falls below D_(U)220 at t=t₃ 166. Oxygen saturation 210 then rises above ND_(U) 230,which triggers an immediate alarm 250 at t=t₄ 268. The alarm 250 remainsactive until oxygen saturation 210 falls below D_(U) 220 at t=t₅ 269.

FIG. 3 illustrates a baseline drift problem with the fixed thresholdalarm schema described above. A person's oxygen saturation is plotted onan oxygen saturation (SpO₂) versus time graph 300. In particular, duringa first time interval T₁ 362, a person has an oxygen saturation 310 witha relatively stable “baseline” 312 punctuated by a shallow, transientdesaturation event 314. This scenario may occur after the person hasbeen on oxygen so that baseline oxygen saturation is near 100%.Accordingly, with a fixed threshold alarm 330 set at, say, 90%, thetransient event 314 does not trigger a nuisance alarm. However, theeffects of oxygen treatments wear off over time and oxygen saturationlevels drift downward 350. In particular, during a second time intervalT₂ 364, a person has an oxygen saturation 320 with a relatively stablebaseline 322. The later baseline 322 is established at a substantiallylower oxygen saturation than the earlier baseline 312. In this scenario,a shallow, transient desaturation event 324 now exceeds the alarmthreshold 330 and results in a nuisance alarm. After many such nuisancealarms, a caregiver may lower the alarm threshold 330 to unsafe levelsor turn off alarms altogether, significantly hampering the effectivenessof monitoring oxygen saturation.

A fixed threshold alarm schema is described above with respect to anoxygen saturation parameter, such as derived from a pulse oximeter.However, problematic fixed threshold alarm behavior may be exhibited ina variety of parameter measurement systems that calculate physiologicalparameters related to circulatory, respiratory, neurological,gastrointestinal, urinary, immune, musculoskeletal, endocrine orreproductive systems, such as the circulatory and respiratory parameterscited above, as but a few examples.

An adaptive alarm system, as described in detail below, advantageouslyprovides an adaptive threshold alarm to solve false alarm and missedtrue alarm problems associated with baseline drift among other issues.For example, for a lower limit embodiment, an adaptive alarm systemadjusts an alarm threshold downwards when a parameter baseline isestablished at lower values. Likewise, for an upper limit embodiment,the adaptive alarm system adjusts an alarm threshold upwards inaccordance with baseline drift so as to avoid nuisance alarms. In anembodiment, the rate of baseline movement is limited so as to avoidmasking of transients. In an embodiment, the baseline is establishedalong upper or lower portions of a parameter envelop so as to provide amargin of safety in lower limit or upper limit systems, respectively.

One aspect of an adaptive alarm system is responsive to a physiologicalparameter so as to generate an alarm threshold that adapts to baselinedrift in the parameter and reduce false alarms without a correspondingincrease in missed true alarms. The adaptive alarm system has aparameter derived from a physiological measurement system using a sensorin communication with a living being. A baseline processor calculates aparameter baseline from an average value of the parameter. Parameterlimits specify an allowable range of the parameter. An adaptivethreshold processor calculates an adaptive threshold from the parameterbaseline and the parameter limits. An alarm generator is responsive tothe parameter and the adaptive threshold so as to trigger an alarmindicative of the parameter crossing the adaptive threshold. Theadaptive threshold is responsive to the parameter baseline so as toincrease in value as the parameter baseline drifts to a higher parametervalue and to decrease in value as the parameter baseline drifts to alower parameter value.

In various embodiments, the baseline processor has a sliding window thatidentifies a time slice of parameter values. A trend calculatordetermines a trend from an average of the parameter values in the timeslice. A response limiter tracks only the relatively long-termtransitions of the trend. A bias calculator deletes the highestparameter values in the time slice or the lowest parameter values in thetime slice so as to adjust the baseline to either a lower value or ahigher value, respectively. The adaptive threshold becomes less responseto baseline drift as the baseline approaches a predefined parameterlimit. A first adaptive threshold is responsive to lower parameterlimits and a second adaptive threshold is responsive to upper parameterlimits. The alarm generator is responsive to both positive and negativetransients from the baseline according to the first adaptive thresholdand the second adaptive threshold. The first adaptive threshold isincreasingly responsive to negative transients and the second adaptivethreshold is decreasingly responsive to positive transients as thebaseline trends toward lower parameter values.

Another aspect of an adaptive alarm system measures a physiologicalparameter, establishes a baseline for the parameter, adjusts an alarmthreshold according to drift of the baseline and triggers an alarm inresponse to the parameter measurement crossing the alarm threshold. Invarious embodiments, the baseline is established by biasing a segment ofthe parameter, calculating a biased trend from the biased segment andrestricting the transient response of the biased trend. The alarmthreshold is adjusted by setting a parameter limit and calculating adelta difference between the alarm threshold and the baseline as alinear function of the baseline according to the parameter limit. Thedelta difference is calculated by decreasing delta as the baselinedrifts toward the parameter limit and increasing delta as the baselinedrifts away from the parameter limit. A parameter limit is set byselecting a first parameter limit in relation to a delayed alarm andselecting a second parameter limit in relation to an un-delayed alarm. Asegment of the parameter is biased by windowing the parametermeasurements, removing a lower value portion of the windowed parametermeasurements and averaging a remaining portion of the windowed parametermeasurements. An upper delta difference between an upper alarm thresholdand the baseline is calculated and a lower delta difference between alower alarm threshold and the baseline is calculated.

A further aspect of an adaptive alarm system has a baseline processorthat inputs a parameter and outputs a baseline according to a trend ofthe parameter. An adaptive threshold processor establishes an alarmthreshold at a delta difference from the baseline. An alarm generatortriggers an alarm based upon a parameter transient from the baselinecrossing the alarm threshold. In various embodiments, a trend calculatoroutputs a biased trend and the baseline is responsive to the biasedtrend so as to reduce the size of a transient that triggers the alarm. Aresponse limiter reduces baseline movement due to parameter transients.The adaptive threshold processor establishes a lower alarm thresholdbelow the baseline and an upper alarm threshold above the baseline sothat the alarm generator is responsive to both positive and negativetransients from the baseline. The baseline processor establishes a lowerbaseline biased above the parameter trend and an upper baseline biasedbelow the parameter trend. The lower alarm threshold is increasinglyresponsive to negative transients and the upper alarm threshold isdecreasingly responsive to positive transients as the baseline trendstoward lower parameter values.

DESCRIPTION OF THE DRAWINGS

FIGS. 1-3 are exemplar graphs illustrating problems and issuesassociated with physiological parameter measurement systems having fixedthreshold alarm schemas;

FIGS. 4A-B are general block diagrams of an adaptive alarm system havinglower parameter limits;

FIGS. 5A-B are a graph of a physiological parameter versus delta spaceand a graph of delta versus baseline, respectively, illustrating therelationship between a baseline, a lower-limit adaptive threshold and avariable difference delta between the baseline and the adaptivethreshold;

FIG. 6 is an exemplar graph of a physiological parameter versus timeillustrating an adaptive alarm system having a lower-limit adaptivethreshold;

FIG. 7 is a graph of oxygen saturation versus time illustrating abaseline for determining an adaptive threshold;

FIG. 8 is a graph of oxygen saturation versus time comparingadaptive-threshold alarm performance with fixed-threshold alarmperformance;

FIGS. 9A-B are general block diagrams of an adaptive alarm system havingupper parameter limits;

FIGS. 10A-B are a graph of a physiological parameter versus delta spaceand a graph of delta versus baseline, respectively, illustrating therelationship between a baseline, an upper-limit adaptive threshold and avariable delta difference between the baseline and the adaptivethreshold;

FIG. 11 is an exemplar graph of a physiological parameter versus timeillustrating an adaptive alarm system having an upper-limit adaptivethreshold;

FIGS. 12A-B are general block diagrams of an adaptive alarm systemhaving both lower alarm limits and upper alarm limits;

FIGS. 13A-E are physiological parameter versus delta space graphsillustrating a lower-limit adaptive threshold, an upper-limit adaptivethreshold, and a combined lower- and upper-limit adaptive threshold invarious delta spaces; and

FIG. 14 is an exemplar graph of a physiological parameter versus timeillustrating an adaptive alarm system having both lower and upper alarmlimits.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

FIGS. 4A-B illustrate an adaptive alarm system 400 embodiment havinglower parameter limits L₁ and L₂. As shown in FIG. 4A, the adaptivealarm system 400 has parameter 401, first limit (L₁) 403, second limit(L₂) 405 and maximum parameter value (Max) 406 inputs and generates acorresponding alarm 412 output. The parameter 401 input is generated bya physiological parameter processor, such as a pulse oximeter or anadvanced blood parameter processor described above, as examples. Theadaptive alarm system 400 has an alarm generator 410, a baselineprocessor 420, and an adaptive threshold processor 440. The alarmgenerator 410 has parameter 401 and adaptive threshold (AT) 442 inputsand generates the alarm 412 output accordingly. A baseline processor 420has the parameter 401 input and generates a parameter baseline (B) 422output. The baseline processor 420, is described in detail with respectto FIG. 4B, below. An adaptive threshold processor 440 has parameterbaseline (B) 422, L₁ 403, L₂ 405 and Max 406 inputs and generates theadaptive threshold (AT) 442. The adaptive threshold processor 440 isdescribed in detail with respect to FIGS. 5A-B, below.

As shown in FIG. 4A, in an embodiment L₁ 403 and L₂ 405 may correspondto conventional fixed alarm thresholds with and without an alarm timedelay, respectively. For an adaptive threshold schema, however, L₁ 403and L₂ 405 do not determine an alarm threshold per se, but are referencelevels for determining an adaptive threshold (AT) 442. In an embodiment,L₁ 403 is an upper limit of the adaptive alarm threshold AT when thebaseline is near the maximum parameter value (Max), and L₂ 405 is alower limit of the adaptive alarm threshold, as described in detail withrespect to FIGS. 5A-B, below. In an exemplar embodiment when theparameter is oxygen saturation, L₁ 403 is set at or around 90% and L₂405 is set at 5 to 10% below L₁, i.e. at 85% to 80% oxygen saturation.Many other L₁ and L₂ values may be used for an adaptive threshold schemaas described herein.

Also shown in FIG. 4A, in an embodiment the alarm 412 output istriggered when the parameter 401 input falls below AT 442 and ends whenthe parameter 401 input rises above AT 442 or is otherwise cancelled. Inan embodiment, the alarm 412 output is triggered after a time delay(TD), which may be fixed or variable. In an embodiment, the time delay(TD) is a function of the adaptive threshold (AT) 442. In an embodiment,the time delay (TD) is zero when the adaptive threshold (AT) is at thesecond lower limit (L₂) 405.

As shown in FIG. 4B, a baseline processor 420 embodiment has a slidingwindow 450, a bias calculator 460, a trend calculator 470 and a responselimiter 480. The sliding window 450 inputs the parameter 401 and outputsa time segment 452 of the parameter 401. In an embodiment, each windowincorporates a five minute span of parameter values. The bias calculator460 advantageously provides an upward shift in the baseline (B) 422 foran additional margin of error over missed true alarms. That is, abaseline 422 is generated that tracks a higher-than-average range ofparameter values, effectively raising the adaptive threshold AT slightlyabove a threshold calculated based upon a true parameter average, asshown and described in detail with respect to FIGS. 7-8, below. In anembodiment, the bias calculator 460 rejects a lower range of parametervalues from each time segment 452 from the sliding window so as togenerate a biased time segment 462.

Also shown in FIG. 4B, the trend calculator 470 outputs a biased trend472 of the remaining higher range of parameter values in each biasedsegment 462. In an embodiment, the biased trend 462 is an average of thevalues in the biased time segment 462. In other embodiments, the biasedtrend 462 is a median or mode of the values in the biased time segment462. The response limiter 480 advantageously limits the extent to whichthe baseline 422 output tracks the biased trend 472. Accordingly, thebaseline 422 tracks only relatively longer-lived transitions of theparameter, but does not track (and hence mask) physiologicallysignificant parameter events, such as oxygen desaturations for a SpO₂parameter to name but one example. In an embodiment, the responselimiter 480 has a low pass transfer function. In an embodiment, theresponse limiter 480 is a slew rate limiter.

FIGS. 5A-B further illustrate an adaptive threshold processor 440 (FIG.4A) having a baseline (B) 422 input and generating an adaptive threshold(AT) 442 output and a delta (Δ) 444 ancillary output according toparameter limits L₁ 403, L₂ 405 and Max 406, as described above. Asshown in FIG. 5A, as the baseline (B) 422 decreases (increases) theadaptive threshold (AT) 444 monotonically decreases (increases) betweenL₁ 403 and L₂ 405. Further, as the baseline (B) 422 decreases(increases) the delta (Δ) 444 difference between the baseline (B) 422and the adaptive threshold (AT) 442 monotonically decreases (increases)between Max−L₁ and zero.

As shown in FIG. 5B, the relationship between the delta (Δ) 444 and thebaseline (B) 444 may be linear 550 (solid line), non-linear 560(small-dash lines) or piecewise-linear (large-dash lines), to name afew. In an embodiment, the adaptive threshold processor 440 (FIG. 4A)calculates an adaptive threshold (AT) 442 output in response to thebaseline (B) 422 input according to a linear relationship. In a linearembodiment, the adaptive threshold processor 440 (FIG. 4A) calculatesthe adaptive threshold (AT) 442 according to EQS. 1-2:

$\begin{matrix}{\Delta = {{{- \left( \frac{{Max} - L_{1}}{{Max} - L_{2}} \right)}\left( {{Max} - B} \right)} + \left( {{Max} - L_{1}} \right)}} & (1) \\{{AT} = {B - \Delta}} & (2)\end{matrix}$where Δ=Max−L₁ @ B=Max; Δ=0 @ B=L₂and where AT=L₁ @ B=Max; AT=L₂ @ B=L₂, accordingly.

FIG. 6 illustrates the operational characteristics an adaptive alarmsystem 400 (FIG. 4A) having parameter limits Max 612, L₁ 614 and L₂ 616and an alarm responsive to a baseline (B) 622, 632, 642; an adaptivethreshold (AT) 628, 638, 648; and a corresponding Δ 626, 636, 646according to EQS. 1-2, above. In particular, a physiological parameter610 is graphed versus time 690 for various time segments t₁, t₂, t₃692-696. The parameter range (PR) 650 is:PR=Max−L ₂  (3)and the adaptive threshold range (ATR) 660 is:ATR=L ₁ −L ₂  (4)

As shown in FIG. 6, during a first time period t₁ 692, a parametersegment 620 has a baseline (B) 622 at about Max 612. As such, Δ626=Max−L₁ and the adaptive threshold (AT) 628 is at about L₁ 614.Accordingly, a transient 624 having a size less than Δ 626 does nottrigger the alarm 412 (FIG. 4A).

Also shown in FIG. 6, during a second time period t₂ 694, a parametersegment 630 has a baseline (B) 632 at about L₁ 614. As such, Δ 636 isless than Max−L₁ and the adaptive threshold (AT) 638 is between L₁ andL₂. Accordingly, a smaller transient 634 will trigger the alarm ascompared to a transient 624 in the first time segment.

Further shown in FIG. 6, during a third time period t₃ 696, a parametersegment 640 has a baseline (B) 642 at about L₂ 616. As such, Δ 646 isabout zero and the adaptive threshold (AT) 648 is at about L₂.Accordingly, even a small negative transient will trigger the alarm. Assuch, the behavior of the alarm threshold AT 628, 638, 648advantageously adapts to higher or lower baseline values so as toincrease or decrease the size of negative transients that trigger or donot trigger the alarm 412 (FIG. 4A).

FIG. 7 is a parameter versus time graph 700 illustrating thecharacteristics of an adaptive alarm system 400 (FIGS. 4A-B), asdescribed with respect to FIGS. 4-6, above, where the parameter isoxygen saturation (SpO₂). The graph 700 has a SpO₂ trace 710 and asuperimposed baseline trace 720. The graph 700 also delineates trackingperiods 730, where the baseline 720 follows the upper portions of SpO₂values, and lagging periods 740, where the baseline 720 does not followtransient SpO₂ events. The tracking time periods 730 illustrate that thebaseline 720 advantageously tracks at the higher range of SpO₂ values710 during relatively stable (flat) periods, as described above. Laggingtime periods 740 illustrate that the baseline 720 is advantageouslylimited in response to transient desaturation events so that significantdesaturations fall below an adaptive threshold (not shown) and triggeran alarm accordingly.

FIG. 8 is a parameter versus time graph 800 illustrating characteristicsof an adaptive alarm system 400 (FIGS. 4A-B), as described with respectto FIGS. 4-6, above, where the parameter is oxygen saturation (SpO₂).Vertical axis (SpO₂) resolution is 1%. The time interval 801 betweenvertical hash marks is five minutes. The graph 800 has a SpO₂ trace 810and a baseline trace 820. The graph 800 also has a fixed threshold trace830, a first adaptive threshold (AT) trace 840 and a second AT trace850. The graph 800 further has a fixed threshold alarm trace 860, afirst adaptive threshold alarm trace 870 and a second adaptive thresholdalarm trace 880. In this example, L₁ is 90% and L₂ is 85% for the firstAT trace 840 and first AT alarm trace 870. L₂ is 80% for a second ATtrace 850 and a second AT alarm trace 880. The fixed threshold 830results in many nuisance alarms 860. By comparison, the adaptivethreshold alarm with L₂=85% has just one time interval of alarms 872during a roughly 6% desaturation period (from 92% to 86%). The adaptivethreshold alarm with L₂=80%, has no alarms during the 1 hour 25 minutemonitoring period.

FIGS. 9A-B illustrate an adaptive alarm system 900 embodiment havingupper parameter limits U₁ and U₂. As shown in FIG. 9A, the adaptivealarm system 900 has parameter 901, first limit (U₁) 903, second limit(U₂) 905 and minimum parameter value (Min) 906 inputs and generates acorresponding alarm 912 output. The parameter 901 input is generated bya physiological parameter processor, such as a pulse oximeter or anadvanced blood parameter processor described above, as examples. Theadaptive alarm system 900 has an alarm generator 910, a baselineprocessor 920, and an adaptive threshold processor 940. The alarmgenerator 910 has parameter 901 and adaptive threshold (AT) 942 inputsand generates the alarm 912 output accordingly. A baseline processor 920has the parameter 901 input and generates a parameter baseline (B) 922output. The baseline processor 920, is described in detail with respectto FIG. 9B, below. An adaptive threshold processor 940 has parameterbaseline (B) 922, U₁ 903, U₂ 905 and Min 906 inputs and generates theadaptive threshold (AT) 942. The adaptive threshold processor 940 isdescribed in detail with respect to FIGS. 10A-B, below.

As shown in FIG. 9A, in an embodiment U₁ 903 and U₂ 905 may correspondto conventional fixed alarm thresholds with and without an alarm timedelay, respectively. For an adaptive threshold schema, however, U₁ 903and U₂ 905 do not determine an alarm threshold per se, but are referencelevels for determining an adaptive threshold (AT) 942. In an embodiment,U₁ 903 is a lower limit of the adaptive alarm threshold AT when thebaseline is near the minimum parameter value (Min), and U₂ 905 is anupper limit of the adaptive alarm threshold, as described in detail withrespect to FIGS. 10A-B, below. In an exemplar embodiment when theparameter is oxygen saturation, U₁ 903 is set at or around 85% and U₂905 is set at or around 90% oxygen saturation. Many other U₁ and U₂values may be used for an adaptive threshold schema as described herein.

Also shown in FIG. 9A, in an embodiment the alarm 912 output istriggered when the parameter 901 input rises above AT 942 and ends whenthe parameter 901 input falls below AT 942 or is otherwise cancelled. Inan embodiment, the alarm 912 output is triggered after a time delay(TD), which may be fixed or variable. In an embodiment, the time delay(TD) is a function of the adaptive threshold (AT) 942. In an embodiment,the time delay (TD) is zero when the adaptive threshold (AT) is at thesecond upper limit (U₂) 905.

As shown in FIG. 9B, a baseline processor 920 embodiment has a slidingwindow 950, a bias calculator 960, a trend calculator 970 and a responselimiter 980. The sliding window 950 inputs the parameter 901 and outputsa time segment 952 of the parameter 901. In an embodiment, each windowincorporates a five minute span of parameter values. The bias calculator960 advantageously provides a downward shift in the baseline (B) 922 foran additional margin of error over missed true alarms. That is, abaseline 922 is generated that tracks a lower-than-average range ofparameter values, effectively lowering the adaptive threshold ATslightly below a threshold calculated based upon a true parameteraverage. In an embodiment, the bias calculator 960 rejects an upperrange of parameter values from each time segment 952 from the slidingwindow so as to generate a biased time segment 962.

Also shown in FIG. 9B, the trend calculator 970 outputs a biased trend972 of the remaining lower range of parameter values in each biasedsegment 962. In an embodiment, the biased trend 962 is an average of thevalues in the biased time segment 962. In other embodiments, the biasedtrend 962 is a median or mode of the values in the biased time segment962. The response limiter 980 advantageously limits the extent to whichthe baseline 922 output tracks the biased trend 972. Accordingly, thebaseline 922 tracks only relatively longer-lived transitions of theparameter, but does not track (and hence mask) physiologicallysignificant parameter events, such as oxygen desaturations for a SpO₂parameter to name but one example. In an embodiment, the responselimiter 980 has a low pass transfer function. In an embodiment, theresponse limiter 980 is a slew rate limiter.

FIGS. 10A-B further illustrate an adaptive threshold processor 940 (FIG.9A) having a baseline (B) 922 input and generating an adaptive threshold(AT) 942 output and a delta (Δ) 944 ancillary output according toparameter limits U₁ 903, U₂ 905 and Min 906, as described above. Asshown in FIG. 10A, as the baseline (B) 922 decreases (increases) theadaptive threshold (AT) 944 monotonically decreases (increases) betweenU₁ 903 and U₂ 905. Further, as the baseline (B) 922 decreases(increases) the delta (Δ) 944 difference between the baseline (B) 922and the adaptive threshold (AT) 942 monotonically decreases (increases)between Min−U₁ and zero.

As shown in FIG. 10B, the relationship between the delta (Δ) 944 and thebaseline (B) 944 may be linear 550 (solid line), non-linear 560(small-dash lines) or piecewise-linear (large-dash lines), to name afew. In an embodiment, the adaptive threshold processor 940 (FIG. 9A)calculates an adaptive threshold (AT) 942 output in response to thebaseline (B) 922 input according to a linear relationship. In a linearembodiment, the adaptive threshold processor 940 (FIG. 9A) calculatesthe adaptive threshold (AT) 942 according to EQS. 5-6:

$\begin{matrix}{\Delta = {{{- \left( \frac{U_{1} - {Min}}{U_{2} - {Min}} \right)}\left( {B - {Min}} \right)} + \left( {U_{1} - {Min}} \right)}} & (5) \\{{AT} = {B + \Delta}} & (6)\end{matrix}$where Δ=U₁−Min @ B=Min; Δ=0 @ B=U₂and where AT=U₁ @ B=Min; AT=U₂ @ B=U₂, accordingly.

FIG. 11 illustrates the operational characteristics an adaptive alarmsystem 900 (FIG. 9A) having parameter limits Min 1112, U₁ 1114 and U₂1116 and an alarm responsive to a baseline (B) 1122, 1132, 1142; anadaptive threshold (AT) 1128, 1138, 1148; and a corresponding Δ 1126,1136, 1146 according to EQS. 5-6, above. In particular, a physiologicalparameter 1110 is graphed versus time 1190 for various time segments t₁,t₂, t₃ 1192-1196. The parameter range (PR) 1150 is:PR=U ₂−Min  (7)and the adaptive threshold range (ATR) 1160 is:ATR=U ₂ −U ₁  (8)

As shown in FIG. 11, during a first time period t₁ 1192, a parametersegment 1120 has a baseline (B) 1122 at about Min 1112. As such, Δ1126=U₁−Min and the adaptive threshold (AT) 1128 is at about U₁ 1114.Accordingly, a transient 1124 having a size less than Δ 1126 does nottrigger the alarm 912 (FIG. 9A).

Also shown in FIG. 11, during a second time period t₂ 1194, a parametersegment 1130 has a baseline (B) 1132 at about U₁ 1114. As such, Δ 1136is less than U₁−Min and the adaptive threshold (AT) 1138 is between U₁and U₂. Accordingly, a smaller transient 1134 will trigger the alarm ascompared to a transient 1124 in the first time segment.

Further shown in FIG. 11, during a third time period t₃ 1196, aparameter segment 1140 has a baseline (B) 1142 at about U₂ 1116. Assuch, Δ 1146 is about zero and the adaptive threshold (AT) 1148 is atabout U₂. Accordingly, even a small positive transient will trigger thealarm. As such, the behavior of the alarm threshold AT 1128, 1138, 1148advantageously adapts to higher or lower baseline values so as toincrease or decrease the size of positive transients that trigger or donot trigger the alarm 912 (FIG. 9A).

FIGS. 12A-B illustrate an adaptive alarm system 1200 embodiment havinglower limits L₁, L₂ 1203, such as described with respect to FIGS. 4A-Babove, or upper limits U₁, U₂ 1205 such as described with respect toFIGS. 9A-B above, or both. As shown in FIG. 12A, the adaptive alarmsystem 1200 has parameter 1201, lower limit 1203 and upper limit 1205inputs and generates a corresponding alarm 1212 output. The parameter1201 input is generated by a physiological parameter processor, such asa pulse oximeter or an advanced blood parameter processor describedabove, as examples. The adaptive alarm system 1200 has an alarmgenerator 1210, a baseline processor 1220 and an adaptive thresholdprocessor 1240. The alarm generator 1210 has parameter 1201 and adaptivethreshold (AT) 1242 inputs and generates the alarm 1212 outputaccordingly. A baseline processor 1220 has the parameter 1201 input andgenerates one or more parameter baseline 1222 outputs. The baselineprocessor 1220, is described in detail with respect to FIG. 12B, below.An adaptive threshold processor 1240 has parameter baseline 1222, lowerlimit L₁, L₂ 1203 and upper limit U₁, U₂ 1205 inputs and generates lowerand upper adaptive threshold AT_(l), A_(u) 1242 outputs. The adaptivethreshold processor 1240 also generates ancillary upper and lower delta1244 outputs. The adaptive threshold processor 1240 is described indetail with respect to FIGS. 13A-E, below.

As shown in FIG. 12A, in an embodiment L₁, L₂ 1203 and U₁, U₂ 1205 maycorrespond to conventional fixed alarm thresholds with an alarm delay(L₁, U₁) and without an alarm delay (L₂, U₂). For an adaptive thresholdschema, however, these limits 1203, 1205 do not determine an alarmthreshold per se, but are reference levels for determining lower andupper adaptive thresholds AT_(l), AT_(u) 1242.

Also shown in FIG. 12A, in an embodiment the alarm 1212 output istriggered when the parameter 1201 input falls below AT_(l) 1242 and endswhen the parameter 1201 input rises above AT, 1242 or the alarm isotherwise cancelled. Further, the alarm 1212 output is triggered whenthe parameter 1201 input rises above AT_(u) 1242 and ends when theparameter 1201 input falls below AT_(u) 1242 or the alarm is otherwisecancelled. In an embodiment, the alarm 1212 output is triggered after atime delay (TD), which may be fixed or variable. In an embodiment, thetime delay (TD) is a function of the adaptive thresholds (AT_(l),AT_(u)) 1242. In an embodiment, the time delay (TD) is zero when thelower adaptive threshold (AT_(l)) 1242 is at the second lower limit (L₂)1203 or when the upper adaptive alarm threshold AT 1242 is at the secondupper limit (U₂) 1205.

As shown in FIG. 12B, a baseline processor 1220 embodiment has a slidingwindow 1250, an over-bias calculator 1260, an under-bias calculator1265, trend calculators 1270 and response limiters 1280. The slidingwindow 1250 inputs the parameter 1201 and outputs a time segment 1252 ofthe parameter 1201. In an embodiment, each window incorporates a fiveminute span of parameter 1201 values.

Also shown in FIG. 12B, the over-bias calculator 1260 advantageouslyprovides an upward shift in the lower baseline (B_(l)) 1282 for anadditional margin of error over missed lower true alarms. That is, alower baseline (B_(l)) 1282 is generated that tracks ahigher-than-average range of parameter values, effectively raising thelower adaptive threshold AT, slightly above a threshold calculated basedupon a true parameter average. In an embodiment, the over-biascalculator 1260 rejects a lower range of parameter values from each timesegment 1252 of the sliding window 1250 so as to generate an over-biasedtime segment 1262.

Further shown in FIG. 12B, the under-bias calculator 1265 advantageouslyprovides a downward shift in the upper baseline (B_(u)) 1287 for anadditional margin of error over missed upper true alarms. That is, anupper baseline (B_(u)) 1287 is generated that tracks alower-than-average range of parameter values, effectively lowering theupper adaptive threshold AT_(u) slightly below a threshold calculatedbased upon a true parameter average. In an embodiment, the under-biascalculator 1267 rejects an upper range of parameter values from eachtime segment 1252 of the sliding window 1250 so as to generate anunder-biased time segment 1267.

Additionally shown in FIG. 12B, the trend calculator 1270 outputs anover-biased trend 1272 of the remaining higher range of parameter valuesin each over-biased segment 1262. Further, the trend calculator 1270outputs an under-biased trend 1277 of the remaining lower range ofparameter values in each under-biased segment 1267. In an embodiment,the biased trends 1272, 1277 are each an average of the values in thecorresponding biased time segments 1262, 1267. In other embodiments, thebiased trends 1272, 1277 are each a median or mode of the values in thecorresponding biased time segments 1262, 1267. The response limiter 1280advantageously limits the extent to which the baseline 1222 outputstrack the biased trends 1272, 1277. Accordingly, the baseline 1222outputs track only relatively longer-lived transitions of the parameter1201, but do not track (and hence mask) physiologically significantparameter events. In an embodiment, the response limiter 1280 has a lowpass transfer function. In an embodiment, the response limiter 1280 is aslew rate limiter.

FIGS. 13A-E illustrate parameter (P) operating ranges and ideal rangesin view of both lower and upper parameter limits. As shown in FIG. 13A,as the baseline (B_(l)) 1317 decreases (increases) the adaptivethreshold (AT_(l)) 1318 monotonically decreases (increases) between L₁and L₂. Further, as the baseline (B_(l)) 1317 decreases (increases) thedelta (Δ_(l)) 1319 difference between the baseline (B_(l)) 1317 and theadaptive threshold (AT_(l)) 1318 monotonically decreases (increases)between Max−L₁ and 0.

As shown in FIG. 13B, as the baseline (B_(u)) 1327 increases (decreases)the adaptive threshold (AT_(u)) 1328 monotonically increases (decreases)between U₁ and U₂. Further, as the baseline (B_(u)) 1327 increases(decreases) the delta (Δ_(u)) 1329 difference between the adaptivethreshold (AT_(u)) 1328 and the baseline (B_(u)) 1327 monotonicallydecreases (increases) between Min−U₁ and 0.

As shown in FIG. 13C, combining FIGS. 13A-B, the parameter (P) operatingrange is bounded by the overlapping regions of 13A and 13B 1330 havingan upper bound of U₂ and a lower bound of L₂. In particular, L₁, L₂ arethe upper and lower limits of the lower adaptive alarm threshold AT_(l);and U₂, U₁ are the upper and lower limits of the upper adaptive alarmthreshold AT_(u).

FIG. 13D illustrates parameter (P) versus the overlapping independentdelta domains F_(u), F_(l) for upper and lower baselines B_(u), B_(l);adaptive thresholds AT_(u), AT_(l) and deltas Δ_(u), Δ_(l), based uponFIGS. 13A-C. FIG. 13E illustrates parameter (P) versus the overlappingindependent delta domains F_(u), F_(l) (reversed); for upper and lowerbaselines B_(u), B_(l); adaptive thresholds AT_(u), AT_(l) and deltasΔ_(u), Δ_(l),

As shown in FIG. 13E, the equations for bi-lateral adaptive thresholdsare:

$\begin{matrix}{\Delta_{u} = {{{- \left( \frac{U_{1} - L_{2}}{U_{2} - L_{2}} \right)}\left( {B - L_{2}} \right)} + \left( {U_{1} - L_{2}} \right)}} & (9) \\{{AT}_{u} = {B + \Delta_{u}}} & (10)\end{matrix}$where Δ_(u)=U₁−L₂ @ B=L₂; and Δ=0 @ B=U₂; andwhere AT_(u)=L₁ @ B=L₂; and AT_(u)=U₂@ B=U₂.Further:

$\begin{matrix}{\Delta_{l} = {\left( \frac{U_{2} - L_{1}}{U_{2} - L_{2}} \right)\left( {B - L_{2}} \right)}} & (11) \\{{AT}_{l} = {B - \Delta_{l}}} & (12)\end{matrix}$where Δ_(l)=U₂−L₁ @ B=U₂; and Δ_(l)=0 @ B=L₂; andwhere AT_(l)=L₁ @ B=U₂; AT_(l)=L₂ @ B=L₂.

Although shown as a linear relationship, in general:Δ_(l)=ƒ₁(B);Δ_(u)=ƒ₂(B)That is, Δ_(l) and Δ_(u) can each be a linear function of B, anon-linear function of B or a piecewise linear function of B, to name afew, in a manner similar to that described with respect to FIGS. 5B and10B, above.

FIGS. 14A-B illustrate the operational characteristics an adaptive alarmsystem 1200 (FIGS. 12A-B) having upper limits U₁, U₂ 1412, 1414 andlower limits L₁, L₂ 1422, 1424. An alarm 1212 (FIG. 12A) output isresponsive to a baseline (B) 1432, 1442, 1452, 1462; an upper delta(Δ_(u)) 1437, 1447, 1457, 1467; and a corresponding upper adaptivethreshold (AT_(u)) 1439, 1449, 1459, 1469, according to EQS. 9-10,above. Further, the alarm 1212 (FIG. 12A) output is responsive to alower delta (Δ_(l)) 1436, 1446, 1456, 1466 and a corresponding loweradaptive threshold (AT_(l)) 1438, 1448, 1458, 1468, according to EQS.11-12, above.

As shown in FIGS. 14A-B, a physiological parameter 1410 is graphedversus time 1490 for various time segments t₁, t₂, t₃, t₄ 1492-1498. Theparameter range (PR) 1480 is:PR=U ₂ −L ₂  (13)the lower adaptive threshold AT_(l) range is:ATR_(l) =L ₁ −L ₂  (14)the upper adaptive threshold AT_(u) range is:ATR_(l) =U ₂ −U ₁  (15)

As shown in FIG. 14A, during a first time period t₁ 1492, a parametersegment 1430 has a baseline (B) 1432 at about U₂ 1414. As such, Δ_(l)1436=U₂−L₁; Δ_(u) 1437=0; AT_(l) 1438=L₁; AT_(u) 1439=U₂. Accordingly, anegative transient 1434 having a size less than U₂−L₁ does not triggeran alarm.

Also shown in FIG. 14A, during a second time period t₂ 1494, a parametersegment 1440 has a baseline (B) 1442 less than U₂. As such, Δ_(l) 1446is less than U₁−L₁ and the adaptive threshold (AT_(u)) 1447 is betweenU₁ and U₂. Accordingly, a smaller negative transient 1444 will triggerthe alarm as compared to the negative transient 1434 in the first timesegment 1430.

Further shown in FIG. 14A, during a third time period t₃ 1496, aparameter segment 1450 has a baseline (B) 1452 less than U₁ 1412. Assuch, a smaller negative transient 1454 will trigger the alarm ascompared to the negative transient 1444 in the second time segment 1440.However, a larger positive transient 1455 is needed to trigger the alarmas compared to the positive transient 1445 in the second time segment1440.

Additionally shown in FIG. 14A, during a fourth time period t₄ 1460, aparameter segment 1460 has a baseline (B) 1462 at about L₂ 1424. Assuch, Δ_(l) 1466=0; Δ_(u) 1467=U₁−L₂; AT_(l) 1468=L₂; AT_(u) 1469=U₁.Accordingly, a positive transient 1465 having a size less than U₁-L₂does not trigger an alarm.

An adaptive alarm system has been disclosed in detail in connection withvarious embodiments. These embodiments are disclosed by way of examplesonly and are not to limit the scope of the claims that follow. One ofordinary skill in the art will appreciate many variations andmodifications.

What is claimed is:
 1. An improved method of obtaining pulse oximetrymeasurements and reducing false alarms therefrom, the method comprising:continuously measuring, with a pulse oximeter in communication with anoptical sensor, a physiological parameter over a first time window;establishing, with the pulse oximeter, a baseline for the physiologicalparameter, the parameter baseline being determined from values of thephysiological parameter taken during a second window of time within thefirst time window, the baseline is reestablished using a new window ofdata on a continuing basis over the first time window; automaticallydetermining, with the pulse oximeter, an upper alarm threshold and alower alarm threshold at a delta difference from a most recent value ofthe baseline, the delta difference for the upper alarm thresholdincreases when the baseline drifts away from a maximum parameter limitand decreases when the baseline drifts toward the maximum parameterlimit, the delta difference for the lower alarm threshold increases whenthe baseline drifts away from a minimum parameter limit and decreaseswhen the baseline drifts toward the minimum parameter limit; dynamicallyadjusting, with the pulse oximeter, at predetermined regular intervalsthe delta difference between the alarm thresholds and the baselineduring the first time window, the delta difference is adjusted based ona current value of the baseline, and the alarm thresholds arere-determined at the predetermined regular intervals over the first timewindow using the current delta difference; and triggering an alarm inresponse to the parameter measurement crossing either of the alarmthresholds.
 2. The method according to claim 1 wherein establishing abaseline comprises: biasing a segment of the parameter measurement;determining a biased trend from the biased segment; and restricting atransient response of the biased trend.
 3. The method according to claim1 wherein adjusting the delta difference comprises: setting a parameterlimit; and determining the delta difference between the alarm thresholdsand the baseline as a linear function of the baseline according to theparameter limit.
 4. The method according to claim 1, further comprisingsetting a parameter limit which includes: selecting a first parameterlimit in relation to a delayed alarm; and selecting a second parameterlimit in relation to an un-delayed alarm.
 5. The method according toclaim 2 wherein biasing a segment of the parameter comprises: windowingthe parameter measurements; removing a lower value portion of thewindowed parameter measurements; and averaging a remaining portion ofthe windowed parameter measurements.
 6. An improved pulse oximeter withreduced false alarms comprising: an input configured to receive one ormore signals from an optical sensor, the optical sensor measuring achange in light absorption of a patient's tissue; and at least onehardware processor, the hardware processor configured to determines aplurality of physiological parameter values over a span of time anddetermines a baseline according to a trend of the parameter values, thebaseline is re-determined at predetermined regular intervals, thepredetermined regular intervals occur when a new plurality ofphysiological parameter values are determined; the at least one hardwareprocessor further configured to establishing an upper and lower alarmthreshold at a delta difference from the baseline at each predeterminedregular interval, the delta difference of the upper alarm thresholdincreasing when the baseline drifts away from a maximum parameter limitand decreasing when the baseline drifts toward the maximum parameterlimit, the delta difference of the lower alarm threshold increasing whenthe baseline drifts away from a minimum parameter limit and decreasingwhen the baseline drifts toward the minimum parameter limit; and analarm generator, the alarm generator is configured to trigger an alarmwhen a parameter transient from the baseline crosses the upper or loweralarm thresholds.
 7. The improved pulse oximeter according to claim 6wherein the at least one hardware processor is further configured tooutputs a biased trend; and the baseline responsive to the biased trendto reduce the size of a transient that triggers the alarm.
 8. Theimproved pulse oximeter according to claim 7 wherein the at least onehardware processor is further configured to reduces baseline movementdue to parameter transients.
 9. The improved pulse oximeter according toclaim 6 wherein the alarm generator is responsive to both positive andnegative transients from the baseline.
 10. The improved pulse oximeteraccording to claim 9 wherein the at least one hardware processor isfurther configured to establishes a lower baseline biased above theparameter trend and an upper baseline biased below the parameter trend.11. The improved pulse oximeter according to claim 10 wherein the loweralarm threshold is increasingly responsive to negative transients andthe upper alarm threshold is decreasingly responsive to positivetransients as the baseline trends toward lower parameter values.